Several researchers have argued that the high intensity of medical care that elderly Medicare beneficiaries receive at the end of life is a potential indicator of poor quality of care and of inefficiency. However, the traditional method for measuring intensity of treatment through the decedent follow-back (case-series) approach may be flawed. Specifically, many decedents were not known to be "dying" and that intensive treatment for them may be appropriate;instead, the goal is to identify patients with poor prognosis for whom intensive care may have low marginal value. We have developed a new measure of end-of-life intensity that focuses on treatment patterns among patients with a high probability of dying (HPD), defined as admissions in the 95th percentile of predicted probability of death. Our HPD measure is a theoretically less biased estimate of decision making for "dying" patients;it compares similar populations across hospitals, and it may be a marker of inefficiency. We plan to use our HPD intensity measure to further elucidate the causes and consequences of race- and condition-specific variations in decision making near the end of life by studying the contribution of individual hospital behavior to these patterns. Blacks have higher rates of life-sustaining treatment (LST) use and patronize hospitals with greater ICU use, and in the aggregate, cancer patients are much less likely than non-cancer patients with serious life limiting illness to receive end-of-life ICU care. Second, we seek to test the performance characteristics of our HPD measure across racial groups and conditions to ascertain whether the measure should be calculated and reported separately for patient subgroups. Third, we seek to explore the generalizability of the HPD approach to the vast majority of administrative data lacking the clinical and risk prediction data available in Pennsylvania. Our aims are:1) To calculate race-specific measures of hospitals'end-of-life treatment intensity and explore the relationship between a hospital's race-specific intensity and post-admission survival;2) To calculate condition-specific measures of hospitals'end-of-life treatment intensity and explore the relationship between a hospital's condition-specific intensity and post-admission survival;and 3) To develop an administrative data-derived HPD measure and compare it to our "gold standard" clinical data-augmented HPD measure. All analyses will use Pennsylvania Health Care Cost Containment Council data linked to state vital statistics data. Our proposed statistical procedures for developing hospital-specific intensity measures will rely on state-of-the-art Bayesian techniques, and survival analyses will extend to health services research the marginal structural models originally developed in epidemiology to address time-varying confounders. The new measure we seek to refine has the potential to fill a niche in current policy efforts to publicly profile hospitals'performance and to help us better understand how decisions to use intensive care and LST vary by race and condition.